Genome-scale metabolic representation of Amycolatopsis balhimycina

Wanwipa Vongsangnak, L. F. Figueiredo, Jochen Förster, Tilmann Weber, Jette Thykær, E. Stegmann, W. Wohlleben, Jens Nielsen

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Infection caused by methicillin‐resistant Staphylococcus aureus (MRSA) is an increasing societal problem. Typically, glycopeptide antibiotics are used in the treatment of these infections. The most comprehensively studied glycopeptide antibiotic biosynthetic pathway is that of balhimycin biosynthesis in Amycolatopsis balhimycina. The balhimycin yield obtained by A. balhimycina is, however, low and there is therefore a need to improve balhimycin production. In this study, we performed genome sequencing, assembly and annotation analysis of A. balhimycina and further used these annotated data to reconstruct a genome‐scale metabolic model for the organism. Here we generated an almost complete A. balhimycina genome sequence comprising 10,562,587 base pairs assembled into 2,153 contigs. The high GC‐genome (∼69%) includes 8,585 open reading frames (ORFs). We used our integrative toolbox called SEQTOR for functional annotation and then integrated annotated data with biochemical and physiological information available for this organism to reconstruct a genome‐scale metabolic model of A. balhimycina. The resulting metabolic model contains 583 ORFs as protein encoding genes (7% of the predicted 8,585 ORFs), 407 EC numbers, 647 metabolites and 1,363 metabolic reactions. During the analysis of the metabolic model, linear, quadratic and evolutionary programming algorithms using flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and OptGene, respectively were applied as well as phenotypic behavior and improved balhimycin production were simulated. The A. balhimycina model shows a good agreement between in silico data and experimental data and also identifies key reactions associated with increased balhimycin production. The reconstruction of the genome‐scale metabolic model of A. balhimycina serves as a basis for physiological characterization. The model allows a rational design of engineering strategies for increasing balhimycin production in A. balhimycina and glycopeptide production in general. Biotechnol. Bioeng. 2012; 109:1798–1807. © 2012 Wiley Periodicals, Inc.
Original languageEnglish
JournalBiotechnology and Bioengineering (Print)
Issue number7
Pages (from-to)1798-1807
Publication statusPublished - 2012


  • Actinomycetales/*genetics/*metabolism Anti-Bacterial Agents/*metabolism Computer Simulation Genome, Bacterial Metabolic Networks and Pathways Models, Biological Vancomycin/*analogs & derivatives/metabolism


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